Top Senior Data Engineer Jobs in Provo, UT

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Leads the design, development, administration, and optimization of enterprise data pipelines and Microsoft Fabric data platforms. Responsibilities include ETL/ELT architecture, medallion layers, data quality, CI/CD, infrastructure as code, security, hybrid connectivity, disaster recovery, performance optimization, and troubleshooting. The role also mentors engineers, establishes technical standards, supports architecture reviews, and provides senior-level guidance for secure, scalable, reliable data platform implementations.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Manages and evolves enterprise data lakes and warehouses, designing scalable AWS-based batch, CDC, and near-real-time ETL/ELT pipelines. Builds data quality, governance, lineage, security, monitoring, and recovery controls; optimizes Spark, Glue, Athena, Redshift, and S3 workloads. Develops reusable Python, SQL, and PySpark frameworks, supports schema evolution and analytical data modeling, and collaborates with business and technical stakeholders to deliver reliable data solutions.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Build and operate production-grade data pipelines and models connecting marketing, event, operational, and SaaS data into Databricks. Own data quality, testing, monitoring, lineage, orchestration, attribution, identity resolution, and governed semantic-layer contributions. Partner cross-functionally on privacy-aware data flows, event taxonomies, and reliable Growth data products while improving engineering standards and maintainability.

Develop and maintain large-scale, cloud-native data services and distributed pipelines for multi-tenant healthcare systems. Responsibilities include data modeling, database component development, SQL programming, performance tuning, testing, deployment, code reviews, proof-of-concepts, and full software development lifecycle ownership. Collaborate with global engineering teams, evaluate technologies, apply security and operational requirements, and use AI tools to accelerate development.

Leads the design, automation, optimization, and maintenance of scalable data pipelines using Airflow, dbt, Python, SQL, and Google Cloud technologies. Manages dbt models, develops reusable architecture patterns, monitors pipeline performance, supports CI/CD and testing, and ensures data security and governance. The role also provides technical leadership, mentors engineers, collaborates cross-functionally, documents implementations, and drives continuous improvement across data engineering practices.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Build and maintain low-latency, highly available distributed data systems and services to deliver high-quality data for product, analytics, and ML. Partner with data scientists and product teams to design scalable, observable, multi-tenant data products using batch and real-time processing frameworks. Ensure 24x7 reliability, monitoring, and operational excellence while owning end-to-end delivery.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Lead database engineering for a federal species-data integration program in a hybrid AWS cloud environment. Responsibilities include harmonizing schemas, documenting source-to-target mappings and lineage, enforcing metadata and data-quality standards, implementing encryption and NIST-aligned security controls, building authoritative source registries, optimizing performance, and leading production migration, validation, monitoring, and operational documentation. The role requires Databricks or equivalent lakehouse expertise, ETL programming, federal governance knowledge, and database migration leadership.

Designs secure Azure data pipelines and analytics foundations using Databricks and ADLS Gen2. Responsibilities include modernizing Oracle workloads, developing medallion data models, implementing data quality and monitoring practices, integrating curated datasets with Power BI, and establishing secure Azure Government identity, network, storage, and access patterns. The role also requires documentation, operational support, stakeholder collaboration, and performance optimization.

Leads data engineering strategy and mentors an analytics engineering team. Designs scalable ELT pipelines, warehouse integrations, data models, API ingestion workflows, and data quality frameworks using cloud and database technologies. Conducts exploratory analysis, supports dashboards and analytical solutions, monitors warehouse performance and costs, and ensures data governance, security, and compliance. Collaborates with data scientists, analysts, engineers, and business stakeholders to deliver healthcare analytics solutions.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

Lead the data engineering strategy and architecture for distributed pharmaceutical data systems. Design and scale pipelines, APIs, and data platforms while ensuring reliability, governance, quality, and PII/PHI protection. Drive technical decisions, improve developer efficiency, deliver business impact, influence cross-functional roadmaps, mentor engineers, communicate with executives, and promote organizational adoption of new technologies and processes.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

Develop business intelligence and analytics solutions using financial, operational, RF, and video performance data. Build scalable data models, dashboards, reports, automation workflows, and ETL/ELT processes using Power BI, Tableau, DataMiner, and Qligent. Analyze trends and anomalies, monitor satellite and video delivery performance, translate business requirements into actionable insights, and collaborate with enterprise stakeholders in a fully remote consulting role.

Design, build, and operate scalable cloud-native data platforms and pipelines supporting analytics, reporting, AI, and customer-facing applications. Develop ETL/ELT workflows, data lakes, event-driven streaming solutions, integrations, and data models using Python, SQL, AWS, databases, warehouses, messaging technologies, and orchestration tools. Monitor production systems, optimize performance, automate testing and deployment, and collaborate with engineering, platform, product, and data teams.

Design, build, and operate scalable cloud-native data platforms and pipelines supporting analytics, reporting, AI workloads, and customer-facing applications. Develop ETL/ELT processes, data lakes, event-driven and streaming solutions, data models, enterprise integrations, and automated monitoring and deployment systems. Collaborate with software, platform, product, and data teams to ensure reliable, secure, high-performance production systems.

Leads the design and development of Mozilla’s data platform, processing tens of terabytes daily. Builds GCP workflows using Dataflow, BigQuery, Pub/Sub, GKE, and Airflow; evaluates technologies; establishes engineering standards; partners with Data Science, Product, Legal, Policy, and Engineering; develops data abstractions; mentors engineers; and supports evolving data products.

Design and improve large-scale data processing and platform systems handling tens of terabytes daily. Build GCP workflows using Dataflow, BigQuery, Pub/Sub, GKE, and Airflow; evaluate technologies; establish engineering standards; lead cross-functional projects; mentor engineers; collaborate with Data Science; and develop data abstractions and active data products.

Designs and builds production data systems, pipelines, database schemas, analytical models, and internal platforms. Responsibilities include writing tested code, optimizing queries and storage, controlling infrastructure costs, enforcing CI/CD, and implementing data testing, monitoring, and alerting. The role also develops AI and LLM-backed data use cases, including retrieval pipelines, embeddings, vector storage, evaluation harnesses, and agentic workflows.